Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and nonalcoholic fatty liver disease: NHANES 2017-2020

doi:10.21203/rs.3.rs-5025893/v1

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Research Article

Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and nonalcoholic fatty liver disease: NHANES 2017-2020

https://doi.org/10.21203/rs.3.rs-5025893/v1

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Background

The ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (NHHR) is strongly linked to various dyslipidemia-related conditions. This study aimed to assess the relationship between NHHR and both nonalcoholic fatty liver disease (NAFLD) and liver fibrosis among American adults.

Methods

Our study utilized data from 5,861 individuals drawn from the National Health and Nutrition Examination Survey (NHANES) 2017–2020 cohort. We employed multivariate logistic regression models to elucidate the association between NHHR and both NAFLD and hepatic fibrosis. To assess the potential nonlinear relationship between NHHR and the risk of NAFLD and hepatic fibrosis, we applied restricted cubic spline (RCS) analysis. Additionally, stratified analyses were conducted to verify the consistency and robustness of the observed associations.

Results

After adjustment for covariates, the weighted multivariable logistic regression analysis identified a robust positive association between NHHR and the incidence of NAFLD (OR = 1.22, 95% CI: 1.14 to 1.30, P < 0.001). In contrast, no significant association was detected between NHHR and liver fibrosis when accounting for potential confounders (P > 0.05). Restricted cubic spline analysis revealed an S-shaped curve characterizing the relationship between NHHR and NAFLD risk (P for nonlinearity < 0.05), with a notable inflection point occurring at 2.49. However, a nonlinear association between NHHR and liver fibrosis was not observed (P for nonlinearity > 0.05). Subgroup analyses further uncovered significant interactions between NHHR and both ethnicity and BMI in relation to liver fibrosis prevalence (P for interaction < 0.05).

Conclusions

The NHHR demonstrated a positive correlation with the prevalence of NAFLD among American adults, whereas no such association was observed with liver fibrosis. Clinically, NHHR may serve as a valuable marker for the early identification of individuals at heightened risk for NAFLD.

NHANE

NAFLD

Hepatic fibrosis

NHHR

Cross-sectional study

Non-alcoholic fatty liver disease (NAFLD) has rapidly emerged as one of the most prevalent chronic liver conditions globally, now affecting approximately a quarter of the world's population, largely due to unhealthy lifestyle factors such as high-energy diets, sedentary behavior, and lack of physical activity [1, 2]. In the United States, NAFLD has similarly become the most common chronic liver disease, affecting over 30% of adults, with its prevalence expected to rise further [3]. NAFLD is characterized by excessive lipid accumulation in the liver in the absence of significant alcohol consumption, and it can progress to more severe conditions including non-alcoholic steatohepatitis (NASH), liver fibrosis, irreversible cirrhosis, and ultimately hepatocellular carcinoma [4, 5]. NAFLD is a form of metabolic stress-induced liver injury, closely linked to insulin resistance (IR) and genetic predisposition [6]. It is strongly associated with type 2 diabetes mellitus (T2DM), hypertension, chronic kidney disease, atherosclerotic cardiovascular disease, and other components of metabolic syndrome (MS), as well as hepatocellular carcinoma [7–10]. Thus, early diagnosis and effective management of NAFLD are crucial to preventing its progression to end-stage liver diseases and associated mortality.

Lipid metabolism, particularly the various forms of cholesterol, plays a central role in the pathogenesis and progression of NAFLD. Patients with NAFLD typically present with dyslipidemia, characterized by elevated serum levels of triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C), along with reduced levels of high-density lipoprotein cholesterol (HDL-C) [11, 12]. Triglycerides accumulate excessively in the liver, leading to hepatic steatosis. When the liver's capacity to oxidize and export these fatty acids is exceeded, it results in lipotoxicity, which triggers inflammation and the subsequent progression to NASH [13, 14]. Conversely, HDL-C participates in reverse cholesterol transport, moving cholesterol from peripheral tissues to the liver, and exerts antioxidant and anti-inflammatory effects that are crucial in mitigating the oxidative stress and inflammation that drive the progression from simple steatosis to NASH and fibrosis [15, 16].

Recent studies have identified the non-high-density lipoprotein to high-density lipoprotein ratio (NHHR) as a novel marker of lipid metabolism. Elevated NHHR has been linked to the development of hyperuricemia [17], non-ST-segment elevation acute myocardial infarction [18], and abdominal aortic aneurysm [19]. However, the relationship between NHHR and NAFLD or hepatic fibrosis in American adults remains unexplored. Therefore, we conducted a nationally representative cross-sectional study using data from the 2017–2020 National Health and Nutrition Examination Survey (NHANES) to investigate the association of NHHR with NAFLD and hepatic fibrosis in the general American population, with the goal of identifying a clinically accessible marker for monitoring NAFLD.

Data source and study population

The National Health and Nutrition Examination Survey (NHANES) is a comprehensive survey that encompasses various ethnic groups and health-related issues across the United States. The survey methodologies received approval from the National Center for Health Statistics (NCHS), and informed consent was obtained from all participants [20]. Among the 15,560 individuals participated in 2017–2020 NHANES cycle, we excluded 5,867 participants under the age of 18, as well as 1,309 individuals without valid transient elastography results, including 348 with ineligible elastography data and 961 for whom elastography was not conducted or data was unavailable. Additionally, we excluded 616 participants with incomplete exams, including 266 with fasting times less than 3 hours, 166 for whom it was not possible to obtain ten measurements, and 184 with IQR/Median liver stiffness measurement (LSM) values ≥ 30%. Furthermore, we excluded 1,482 participants with other common liver diseases, including 40 with hepatitis B, 165 with hepatitis C, and 1,277 with significant alcohol consumption defined as > 2 standard drinks per day for women and > 3 standard drinks per day for men, along with 425 individuals lacking available NHHR data. Ultimately, our final analysis comprised 5,861 participants (Fig. 1).

Assessment of NHHR

In this study, the non-HDL-C to HDL-C ratio (NHHR) was employed as the primary exposure variable. NHHR is calculated as the ratio of non-HDL-C to HDL-C. Non-HDL-C is derived by subtracting HDL-C from total cholesterol (TC) using blood samples collected from fasting participants [17, 21]. The levels of TC and HDL-C were measured through enzymatic assays using an automated biochemical analyzer.

Evaluation of NAFLD and hepatic fibrosis

The most reliable method for diagnosing NAFLD and evaluating the extent of hepatic steatosis and fibrosis is liver biopsy. However, performing liver biopsies on large patient cohorts is often impractical due to issues related to patient acceptability, cost, and associated risks [22]. In recent years, vibration-controlled transient elastography (VCTE) has emerged as a leading non-invasive and cost-effective tool for assessing hepatic fibrosis and steatosis in chronic liver diseases [23]. In this study, experienced NHANES staff conducted VCTE on each participant using the FibroScan®-equipped model 502 V2 Touch. The VCTE report provided an assessment of hepatic steatosis via the controlled attenuation parameter (CAP) and hepatic fibrosis through liver stiffness measurement (LSM) [24]. VCTE results were deemed valid when at least 10 LSMs were obtained with more than 3 hours of fasting and an interquartile range/median (IQR/Median) ratio of less than 30% [25]. According to established thresholds from previous studies, NAFLD was diagnosed with a CAP value of ≥ 274 dB/m [26], with severe hepatic steatosis indicated by a CAP value of ≥ 302 dB/m [27]. Hepatic fibrosis was categorized into stages F2, F3, and F4, corresponding to LSM thresholds of 8.2, 9.7, and 13.6 kPa, respectively [28].

Covariates definitions

The covariates included in this study encompass demographic information, examination data, laboratory results, and questionnaire responses. Demographic information comprised age, race, educational level, and the ratio of family income to poverty (PIR). Examination data included body mass index (BMI) and waist circumference (WC). Laboratory data encompassed measurements of alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), albumin, ferritin, glycated hemoglobin (HbA1c), C-reactive protein (CRP), creatinine, total bilirubin, uric acid, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C). Questionnaire data covered smoking status, moderate physical activity, hypertension, and diabetes. To address potential sample size bias, the original NHANES categories "other Hispanic" and "other multiracial race" were combined into a single "other race" variable. Smoking behavior, physical activity, hypertension, and diabetes were assessed using specific questions: "Have you smoked at least 100 cigarettes in your lifetime?", "Does moderate-intensity activity cause slight increases in breathing or heart rate?", "Has a doctor ever diagnosed you with high blood pressure?", and "Has a doctor ever diagnosed you with diabetes?" Further details on these covariates are available on the NHANES website (https://www.cdc.gov/nchs/nhanes/).

Statistical analysis

All analyses incorporated NHANES sampling weights. Participants were categorized into four subgroups based on NHHR quartiles. Continuous variables were reported as weighted means and weighted standard deviations for normally distributed data, or as medians and interquartile ranges (IQR) for non-normally distributed data. Categorical variables were presented as weighted proportions. To compare differences among the four subgroups, one-way analysis of variance (ANOVA) was used for normally distributed data, the Kruskal-Wallis test for non-normally distributed data, and the chi-square test for categorical data. Spearman correlation analyses were subsequently performed to assess the relationship between NHHR and clinical variables. A weighted multivariable linear regression model was employed to examine the association of NHHR with NAFLD and hepatic fibrosis across the subgroups, using the first quartile (Q1) as the reference standard. To further explore these associations, we developed three models: Model 1 was unadjusted; Model 2 adjusted for age, gender, and race; and Model 3 further adjusted for all potential confounders, including age, gender, race, educational level, marital status, smoking, moderate physical activity, hypertension, diabetes, PIR, WC, BMI, ALT, AST, GGT, albumin, ferritin, HbA1c, CRP, creatinine, total bilirubin, and uric acid. Additionally, dose-response relationships between NHHR and the incidence of NAFLD and hepatic fibrosis were evaluated using restricted cubic spline (RCS) analyses. Subgroup analyses were conducted to assess the stability of the associations across different cohorts and to identify sensitive populations, stratified by age, gender, race, BMI, hypertension, and diabetes. Interaction tests were further performed to detect heterogeneity in the relationships within each subgroup. All statistical analyses were carried out using R (http://www.R-project.org, The R Foundation) and EmpowerStats software (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA), with a significance threshold set at P < 0.05.

Participants characteristics

As detailed in Table 1, the study included 5,861 participants with a mean age of 49.11 ± 17.80 years. The cohort comprised 48.74% males and 51.26% females, with 63.04% identified as non-Hispanic white. The prevalence of NAFLD and hepatic fibrosis was 42.54% and 8.17%, respectively. Higher NHHR levels were significantly associated with an increased prevalence of NAFLD, severe hepatic steatosis, hepatic fibrosis, as well as elevated CAP and LSM values (P < 0.05). Factors such as age, sex, ethnicity, smoking status, household income, educational attainment, BMI, WC, hypertension, diabetes, ALT, AST, GGT, albumin, ferritin, HbA1c, CRP, total bilirubin, creatinine, uric acid, TC, HDL-C, TG, and LDL-C levels showed significant variation across all NHHR quartiles (P < 0.05). However, no significant differences were observed across NHHR quartiles in terms of moderate physical activity (P = 0.465).

Relationships between NHHR and clinical variables

As shown in Table 2, NHHR exhibited a positive correlation with BMI, WC, ALT, AST, GGT, ferritin levels, HbA1c, CRP, creatinine, uric acid, TC, TG, LDL-C, CAP, and LSM (r = 0.249, 0.279, 0.231, 0.082, 0.126, 0.177, 0.141, 0.061, 0.054, 0.223, 0.465, 0.607, 0.642, 0.288, and 0.076, respectively, P < 0.05). In contrast, NHHR was negatively correlated with total bilirubin and HDL-C (r = -0.065 and − 0.630, respectively, P < 0.05). However, no significant negative association was found with age and albumin (r = -0.009 and − 0.011, P = 0.514 and 0.383, respectively).

Association between NHHR and NAFLD and hepatic fibrosis

The results of the multivariate regression analysis are summarized in Table 3. Across all models, NHHR demonstrated a positive correlation with NAFLD. When NHHR was divided into quartiles, a consistent trend was observed, with higher NHHR levels showing a stronger association with NAFLD (P for trend < 0.001). Compared to participants in the lowest quartile (Q1), those in Q2, Q3, and Q4 had significantly higher odds ratios (ORs) for NAFLD: 1.65 (95% CI, 1.40 to 1.94), 3.01 (95% CI, 2.57 to 3.52), and 4.98 (95% CI, 4.25 to 5.84), respectively (P < 0.05). Furthermore, after adjusting for other clinical variables in the multivariate logistic regression analysis, the ORs for NAFLD in Q2, Q3, and Q4 relative to Q1 were 1.26 (95% CI, 1.02 to 1.56), 1.61 (95% CI, 1.30 to 2.00), and 2.05 (95% CI, 1.64 to 2.57), respectively (P < 0.05). In contrast, the ORs for hepatic fibrosis in participants in Q2, Q3, and Q4 compared to those in Q1 were 1.16 (95% CI, 0.88 to 1.52) (P > 0.05), 1.32 (95% CI, 1.01 to 1.73) (P < 0.05), and 1.76 (95% CI, 1.36 to 2.27) (P < 0.05), respectively. However, after adjusting for other clinical variables, the ORs for hepatic fibrosis were no longer significant (P > 0.05).

Dose-response relationships between NHHR and NAFLD and hepatic fibrosis

As illustrated in Fig. 2, NHHR demonstrated a nonlinear association with the prevalence of NAFLD (P for nonlinearity < 0.05). Specifically, the risk of NAFLD increased with higher NHHR levels up to a threshold of 2.49, beyond which this trend plateaued. Conversely, as shown in Fig. 3, no significant nonlinear relationship was observed between NHHR and liver fibrosis (P for nonlinearity > 0.05).

Subgroup analysis

To assess potential modifiers of the relationship between NHHR and NAFLD as well as liver fibrosis, subgroup analyses stratified by age, sex, race, BMI, smoking status, hypertension, and diabetes are depicted in Figs. 4 and 5. The data revealed a generally consistent positive correlation between NHHR and NAFLD across most populations. However, this association was not significant in non-Hispanic Black individuals, other racial groups, and participants with a BMI ≥ 30 (P > 0.05). Additionally, an interaction was identified between ethnicity and NHHR, as well as between BMI and NHHR, in relation to the prevalence of liver fibrosis (P for interaction < 0.05).

The current study explored the associations between NHHR and both NAFLD and hepatic fibrosis, with findings indicating a positive relationship between NHHR and NAFLD across all models. However, while NHHR was positively correlated with liver fibrosis in all models, this correlation was not statistically significant in Model 3. Notably, an S-shaped association was observed between NHHR and NAFLD, with a critical inflection point identified at 2.49.

The rising prevalence of NAFLD underscores the urgent need for accurate, non-invasive diagnostic methods, particularly as liver biopsy, while the gold standard for diagnosis and staging, is invasive, costly, and carries a risk of complications [29]. VCTE has gained prominence as a leading non-invasive alternative, widely recognized for its reliable assessment of hepatic steatosis and fibrosis through the measurement of CAP and LSM [30, 31]. These techniques have demonstrated a high degree of accuracy, comparable to that of liver biopsy, making them essential tools in the clinical management of NAFLD, especially in scenarios requiring repeated monitoring [32].

The pathogenesis of NAFLD is multifactorial, with IR and dysregulated lipid metabolism being central to disease progression [4, 33]. Previous studies have established that metabolic syndrome, obesity, and type 2 diabetes mellitus (T2DM) are significant risk factors for NAFLD [34–36]. In the liver, IR promotes de novo lipogenesis, impairs triglyceride clearance, and leads to the overproduction of very low-density lipoproteins (VLDLs) rich in triglycerides. This results in the accumulation of triglycerides within hepatocytes, contributing to hepatic steatosis [37, 38]. Furthermore, IR drives the secretion of larger, triglyceride-enriched VLDL particles and significantly reduces HDL-C concentrations, exacerbating lipid imbalances [39]. Recent studies have also highlighted a strong association between IR and leptin secretion [40]. Persistent hyperleptinemia has been linked to fatty liver, hepatic fibrosis, and hepatocellular carcinoma in several observational clinical studies, suggesting it may be a reliable marker for the onset or progression of NAFLD [41, 42]. Currently, leptin is believed to contribute to NAFLD development by centrally inhibiting food intake while enhancing sympathetic nervous activity, metabolic rate, and gluconeogenesis [43, 44].

In addition to lipid abnormalities, oxidative stress and inflammation play pivotal roles in the progression of NAFLD to more severe liver diseases. Oxidized low-density lipoprotein (oxLDL) is a key mediator of these pathogenic processes. A recent study by Ampuero et al. revealed that the oxLDL antibodies/HDL-C ratio was significantly higher in lean NAFLD patients [45]. The accumulation of oxLDL in the liver triggers a cascade of inflammatory responses, including the activation of hepatic macrophages (Kupffer cells) and the secretion of pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6). These cytokines exacerbate insulin resistance, creating a feedback loop that leads to further lipid accumulation and hepatocellular damage [46, 47]. The oxidative stress associated with NAFLD has been shown to contribute to mitochondrial dysfunction and lipid peroxidation, with the overproduction of reactive oxygen species (ROS) further amplifying cellular injury and promoting the progression of liver disease [48]. Emerging evidence suggests that excess intracellular cholesterol activates liver X receptors (LXRs), leading to increased VLDL secretion and the formation of small, dense LDL particles. These particles are more prone to oxidation and have reduced affinity for LDL receptors, thereby enhancing their atherogenic potential and contributing to the pro-inflammatory environment within the liver [49]. Additionally, Mocciaro has identified low levels of HDL-C as a risk factor for NAFLD. Low HDL-C levels reduce the body's ability to transport cholesterol back to the liver, making LDL cholesterol more prone to oxidation [50], a finding that aligns with the study conducted by Karami [51].

Existing research has indicated that NHHR is a better predictor than non-HDL-C for cardiovascular disease (CVD) in individuals with type 2 diabetes [52], and it is also a superior predictor for IR and MS compared to the apoB/apoA1 ratio [53]. Two studies have shown that NHHR outperforms non-HDL-C as an independent predictor of new-onset NAFLD in both Chinese adults and adolescents [54, 55]. Therefore, it is essential to investigate the association between NHHR and NAFLD and hepatic fibrosis in the general American adult population. Our study aligns with previous research and contributes to the understanding of the causal relationship between dyslipidemia and NAFLD.

Strengths and limitations

The strengths of this study include its large sample size and the relatively reliable assessments of hepatic steatosis and fibrosis. Notably, this is the first study to identify a positive association between NHHR and both NAFLD and liver fibrosis in the general American population. Given its simplicity and ease of determination, NHHR may prove valuable in identifying NAFLD in adults. However, several limitations must be acknowledged. First, the cross-sectional design limits the ability to draw definitive causal inferences between NHHR and NAFLD or liver fibrosis. Future longitudinal studies are necessary to better explore potential causal relationships. Second, as this study was conducted exclusively in American adults, the generalizability of the findings to other populations or ethnic groups is uncertain due to genetic and environmental differences. Third, while liver biopsy is the gold standard for grading hepatic steatosis and fibrosis, NAFLD in this study was diagnosed using ultrasonography rather than histopathology, which may introduce diagnostic inaccuracies. Finally, due to limitations of the NHANES database, some confounding factors that could influence the results were not accounted for. Therefore, further prospective clinical and basic research is needed to validate these findings and address the aforementioned limitations.

In the American adult population, NHHR exhibited a nonlinear positive association with the prevalence of NAFLD, while no such relationship was observed with liver fibrosis. These findings suggest that NHHR could serve as a sensitive biomarker for the early detection of NAFLD, offering potential utility in informing and guiding therapeutic strategies.

Acknowledgments

We acknowledge the data provided by the National Health and Nutrition Examination Survey (NHANES).

Author contributions

Z.S. conducted and interpreted the statistical analyses and drafted the initial manuscript. Y.Y. was responsible for data collection and statistical analysis. P.L. designed the study and revised the manuscript. All authors reviewed and approved the final manuscript.

Funding

This research did not receive any direct funding from third-party donors or funding institutions, whether public, commercial, or non-profit.

Data availability

The dataset analyzed in this study is publicly available at NHANES: https://www.cdc.gov/nchs/nhanes/.

Ethics approval and consent to participate

In compliance with the Declaration of Helsinki, the ethics review board of the National Center for Health Statistics approved all NHANES protocols. Written informed consent was obtained from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Table 1 to 3 are available in the Supplementary Files section.

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Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and nonalcoholic fatty liver disease: NHANES 2017-2020

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Abstract

Background

Methods

Results

Conclusions

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Introduction

Materials and methods

Data source and study population

Assessment of NHHR

Evaluation of NAFLD and hepatic fibrosis

Covariates definitions

Statistical analysis

Results

Participants characteristics

Relationships between NHHR and clinical variables

Association between NHHR and NAFLD and hepatic fibrosis

Dose-response relationships between NHHR and NAFLD and hepatic fibrosis

Subgroup analysis

Discussion

Strengths and limitations

Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

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